{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "original_data_path = \"../original_data/bank-additional-full.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_data_small = pd.read_csv(original_data_path,sep=\";\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数据的基本情况，一共21列，最后一列为要预测的目标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null object\n",
      "marital           41188 non-null object\n",
      "education         41188 non-null object\n",
      "default           41188 non-null object\n",
      "housing           41188 non-null object\n",
      "loan              41188 non-null object\n",
      "contact           41188 non-null object\n",
      "month             41188 non-null object\n",
      "day_of_week       41188 non-null object\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null object\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null object\n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(bank_data_small.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数值型数据的分布情况，可以看到数值型属性中不存在缺失值，数据均值差别比较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               age      duration      campaign         pdays      previous  \\\n",
      "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
      "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
      "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
      "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
      "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
      "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
      "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
      "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
      "\n",
      "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
      "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
      "mean       0.081886       93.575664     -40.502600      3.621291   5167.035911  \n",
      "std        1.570960        0.578840       4.628198      1.734447     72.251528  \n",
      "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
      "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
      "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
      "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
      "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  \n"
     ]
    }
   ],
   "source": [
    "print(bank_data_small.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看字符型属性缺失情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of \"unknown\" in job： 330 / 41188\n",
      "Percentage of \"unknown\" in marital： 80 / 41188\n",
      "Percentage of \"unknown\" in education： 1731 / 41188\n",
      "Percentage of \"unknown\" in default： 8597 / 41188\n",
      "Percentage of \"unknown\" in housing： 990 / 41188\n",
      "Percentage of \"unknown\" in loan： 990 / 41188\n",
      "Percentage of \"unknown\" in contact： 0 / 41188\n",
      "Percentage of \"unknown\" in month： 0 / 41188\n",
      "Percentage of \"unknown\" in day_of_week： 0 / 41188\n",
      "Percentage of \"unknown\" in poutcome： 0 / 41188\n",
      "Percentage of \"unknown\" in y： 0 / 41188\n"
     ]
    }
   ],
   "source": [
    "for col in bank_data_small.columns:\n",
    "    if bank_data_small[col].dtype == object:\n",
    "        print(\"Percentage of \\\"unknown\\\" in %s：\" %col ,bank_data_small[bank_data_small[col] == \"unknown\"][col].count(),\"/\",bank_data_small[col].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11265417111780131\n",
      "job\n",
      "admin.           0.129726\n",
      "blue-collar      0.068943\n",
      "entrepreneur     0.085165\n",
      "housemaid        0.100000\n",
      "management       0.112175\n",
      "retired          0.252326\n",
      "self-employed    0.104856\n",
      "services         0.081381\n",
      "student          0.314286\n",
      "technician       0.108260\n",
      "unemployed       0.142012\n",
      "unknown          0.112121\n",
      "dtype: float64\n",
      "marital\n",
      "divorced    0.103209\n",
      "married     0.101573\n",
      "single      0.140041\n",
      "unknown     0.150000\n",
      "dtype: float64\n",
      "education\n",
      "basic.4y               0.102490\n",
      "basic.6y               0.082024\n",
      "basic.9y               0.078246\n",
      "high.school            0.108355\n",
      "illiterate             0.222222\n",
      "professional.course    0.113485\n",
      "university.degree      0.137245\n",
      "unknown                0.145003\n",
      "dtype: float64\n",
      "default\n",
      "no         0.12879\n",
      "unknown    0.05153\n",
      "yes        0.00000\n",
      "dtype: float64\n",
      "housing\n",
      "no         0.108796\n",
      "unknown    0.108081\n",
      "yes        0.116194\n",
      "dtype: float64\n",
      "loan\n",
      "no         0.113402\n",
      "unknown    0.108081\n",
      "yes        0.109315\n",
      "dtype: float64\n",
      "contact\n",
      "cellular     0.147376\n",
      "telephone    0.052313\n",
      "dtype: float64\n",
      "month\n",
      "apr    0.204787\n",
      "aug    0.106021\n",
      "dec    0.489011\n",
      "jul    0.090466\n",
      "jun    0.105115\n",
      "mar    0.505495\n",
      "may    0.064347\n",
      "nov    0.101439\n",
      "oct    0.438719\n",
      "sep    0.449123\n",
      "dtype: float64\n",
      "day_of_week\n",
      "fri    0.108087\n",
      "mon    0.099483\n",
      "thu    0.121188\n",
      "tue    0.117800\n",
      "wed    0.116671\n",
      "dtype: float64\n",
      "poutcome\n",
      "failure        0.142286\n",
      "nonexistent    0.088322\n",
      "success        0.651129\n",
      "dtype: float64\n",
      "y\n",
      "no     0.0\n",
      "yes    1.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#正类样本占总样本数的比例大概为1/9\n",
    "print(bank_data_small['y'][bank_data_small['y']== 'yes'].count()/bank_data_small['y'].count())\n",
    "#字符型属性各个属性值所占的比例\n",
    "for col in bank_data_small.columns:\n",
    "    if bank_data_small[col].dtype == object:\n",
    "        print(bank_data_small.groupby(bank_data_small[col]).apply(lambda x: x['y'][x['y']== 'yes'].count()/x['y'].count()))      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b65ffbc080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#print(bank_data_small[bank_data_small['y']=='yes']['age'].describe())\n",
    "#print(bank_data_small[bank_data_small['y']=='no']['age'].describe())\n",
    "\n",
    "kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=40)\n",
    "plt.hist(bank_data_small[bank_data_small['y']=='yes']['age'],label = \"Yes\", **kwargs)\n",
    "plt.hist(bank_data_small[bank_data_small['y']=='no']['age'],label = \"No\", **kwargs)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b65b3389e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "job = bank_data_small[bank_data_small['y']=='yes'].groupby('job').count()['y'].index\n",
    "job_count_yes = bank_data_small[bank_data_small['y']=='yes'].groupby('job').count()['y']\n",
    "job_count_no = bank_data_small[bank_data_small['y']=='no'].groupby('job').count()['y']\n",
    "#print(job.values)\n",
    "#print(job_count_yes)\n",
    "#print(job_count_no)\n",
    "width = 0.5\n",
    "plt.figure(figsize=(8, 4)) \n",
    "plt.subplot(121)\n",
    "plt.bar(job,job_count_yes, width, color='green', label='Yes')\n",
    "plt.bar(job,job_count_no, width, bottom = job_count_yes, color='red', label='No')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.bar(job,job_count_yes/job_count_no, width, color='blue', label='Yes/No')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b65c4dedd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marital = bank_data_small[bank_data_small['y']=='yes'].groupby('marital').count()['y'].index\n",
    "marital_count_yes = bank_data_small[bank_data_small['y']=='yes'].groupby('marital').count()['y']\n",
    "marital_count_no = bank_data_small[bank_data_small['y']=='no'].groupby('marital').count()['y']\n",
    "#print(marital.values)\n",
    "#print(marital_count_yes.values)\n",
    "#print(marital_count_no)\n",
    "plt.figure(figsize=(8, 4)) \n",
    "plt.subplot(121)\n",
    "plt.title('Yes')\n",
    "plt.pie(marital_count_yes.values,labels = marital)\n",
    "plt.subplot(122)\n",
    "plt.title('No')\n",
    "plt.pie(marital_count_no.values,labels = marital)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hZMm8TVOoRDGWVGxCtcXjKKCuEwUaU36GYcQOGRkwYADbzmrGpG2n0K5d/qbbc14iZ6TNZNXM3GZyNCIFU36GYcQO48fDsmUML9+FkiXhyitzvOOoVL6lOYVQlnw4ISjiGeHDlJ9hGLFDUhJatiw95txAmzZQrFj+pqvR9jz2SHH2j7ZUZ9GGKT/DMGKDf/+Fr79m1UXtWbetWJ69PAORIgksq3whJy63c79ow5SfYRixwaefQmoqSXShTBm4/PKcb8kN+y5szmn757Ho903BmdAIC6b8DMMo+KhCUhIZ5zbm3d/O4rrroEiR4Exd9bZEAFYMGh+cCY2wYMrPMIyCz19/wYIFzD6vCzt3EhSTZyZVrz6HXVKKQuPt3C+aMOVnGAUBO3A6OklJULIkb2+4mQoVoEWLIM4dH8/y6s04afU49u8P4rxGSDHlZxjRztKlULMmvP++35JEJjt2wNChpN10K1/9XJIbboDChYO7xP5mzambsZj5v/wT3ImNkGHKzzCimeRkuO46WL0a/vtfWLvWb4kij88/h717+a1uF5KTg2vyzKRGR3fut+ZjS3UWLZjyM4xoRRXuvBMWLID+/V2Znkcf9VuqyCMpCerXp+/Uc6hcGS6+OPhLHNeiPjsKlaPw73buFy2Y8jOMaOXNN2HoUHjxRejSBf73Pxg2DMaM8VuyyGH6dJg5k5T2XfjhR+HGGyEuLgTrFCrEypqXcPK6caSmhmB+I+iY8jOMaGTsWGfmvOEG9wrw+ONQuzY88AD2BPZISoJixRhZ6jZSUkJj8jxA8+bU1JXM/mZFCBcxgoUpP8OINlavdk/xU0+FQYNAxPUXLQpvvw2LFkHv3v7KGAns3u3O+268kU+/L0u1atCkSeiWq3mHO/db/7md+0UDpvwMI5pISXG7vdRUGDECSpU69Hrr1nDNNfD887BmjT8yRgrDhsGuXexs14Wff4abboJCIXzilbngdLbEV6LoXwVb+YnIIyIyX0TmicgXIlJURGqJyGQRWSIiQ0UkwRtbxPu81LteM2Cep7z+RSISpHw7uceUn2FEC6pw330wbRp88gmcfHLW4956y5Xu6dYtvPJFGklJcNppDF9/IWlp5Lt8UY6IsKZ2ImdsGsue5IIZdyki1YAHgUaqeiYQB7QDXgF6q2pdYBtwp3fLncA2Va0D9PbGISKne/edAbQC3heRUJzGZospP8OIFvr1c2bOZ56BNm2yH1ezJnTvDl99BaNHh028iGLePJg0Ce66i6HDhFq14NxzQ79sfMvmVGMds75cEvrF/CMeKCYi8UBxYD3QHPjKu/4RcK33/hrvM971FiIiXv8QVd2nqiuApcB5YZIfCKHyE5GBIrJJROYF9L0mIn+LyBwRGSEiZb3+miKyV0Rmea1vwD3niMhcb3vcx/viDCO2+OsvePBBV4CuZ8+cxz/2GNSp45xf9u0LuXgRx4ABkJDAltYdGDPGmTzD8eSo1dmd+20aUjBDHlT1H+B1YDVO6e0ApgPbVTXdG7YWqOa9rwas8e5N98ZXCOzP4p6wEMqd32DcdjaQ0cCZqloPWAw8FXBtmao28No9Af0fAF2Bul47fE7DKNisX+/O+WrUcJUJcnNwVbQovPMOLF4ce84vKSnOLHzddXw1/jj27w+DydOjRP06bEqoRsmpUXvuFy8i0wJa18CLIlIOt2urBVQFSgBXZDFPpt03qz859Cj9YSNkyk9VJwBbD+v7JeCvg0lA9aPNISJVgNKq+peqKvAxB7fThlHwSU2FG290KbpGjIBy5XJ/b6tWLvtLr17OQzRW+Ppr2LoVunRh6FB3NFq/fpjWFmHdKc2pt3UcO7ZH5blfuqo2Cmj9D7t+KbBCVTerahrwNdAEKOuZQcE919d579cCJwB418vg9MKB/izuCQt+nvl1Bn4K+FxLRGaKyG8icpHXVw33JWUS9q2xYfjKo4/CH3/Ahx/CWWcd+/29eztHmVhyfklKgpNOYsNpifz2m4sKCedhSdErEqnEZmZ+Oj98i4aP1cD5IlLcO4JqASwAxgFtvTEdgW+99yO9z3jXx3obmZFAO88btBbOqjclTD8D4JPyE5HuQDrwmde1HqihqmcD3YDPRaQ0x7g1FpGumdv19PT07IYZRnTw8cfw7rtOAebVbnfiifD00zB8OIwaFVz5IpElS2D8eLjrLr76uhAZGSEObM+Cmp2bA7D1q4J37qeqk3GOKzOAuTgd0h94AugmIktxZ3oferd8CFTw+rsBT3rzzAeG4RTnz8D9qhrWmhiiISyF4sV0fO+5xGb2dQTuAVqo6p5s7hsPPAb8A4xT1VO9/luAS1T17pzWLlGihCYnJ+f3RzAMf5gxAy68EC64AH75BeLjc74nO/btO7hrnDs3eFVcI5EnnoA33oA1a2h6YxV27HA/crhZX+wkFibUp/mOEeFfPB+IyB5VLeG3HOEgrDs/EWmF+wuhTaDiE5GKmTEeInISbgu8XFXXA7tE5Hxvi92Bg9tpwyiY/PuvO6urWNHl7syP4gOn7N55x+2K3ngjODJGIqmpMHgwXHUVa9Kr8Mcf4d/1ZbLxjOacvXM8mzdYgb9IJZShDl8AfwGniMhaEbkTeBcoBYw+LKShGTBHRGbjttT3qGqms8y9wABcHMgyDj0nNIyCRXq6M3Fu3OgcNypWDM68l18O118PL7wAq1YFZ85I47vvYNMm6NKFL790XX4pvxJXJ1KO7cz6aLY/Ahg5ElKzp5+Y2dOISp54Al59FQYOhDvuCO7cq1e7fKCtWjnFWtBo1Qrmz4eVK2ncJI60NGc99oP01euIP7Eaw89/jRv+eswfIfKAmT0Nwwg/X37pFN+99wZf8YGLE3zmGRcy8fPPwZ/fT1audGejnTuzfFUcU6aEL7YvK+JrVGVtiVOoMDdq4/0KPKb8DCMSmD/fKbwLLnC5OUNFt24u8O0//ylYmV8GDnSvnTszbJh7e9NN/okDsKV+c85JnsDaFWn+CmJkiSk/w/Cb7dudg0upUi4fZ0JC6NbKdH5ZuhRefz1064ST9HSn/C6/HE48kaFDoXFjl+LUT8pcm0gpdjN38HR/BTGyxJSfYfhJRga0bw8rVjizZ9WqoV+zZUto2xb+7/+cuTDaGTUK/vkHunRh0SKYNcs/R5dAanS4BIDd35vpMxIx5WcYftKrF3z/vcvE0rRp+NZ9802X9uSRR8K3ZqhISoLKleHqqxk61P1Yfps8AQpVrsiqMmdx/PyxFFC/wqjGlJ9h+MX337sKDR06wP33h3ftE06AZ5+Fb76BH38M79rBZP169z126gSFCzN0qPsbolqEJEHc0bA55+z7g2ULCtD5agHBlJ9h+MGSJXD77dCwIfTtG97kk5k88giccoorlZSSEv71g8GgQbB/P9x5J/PmwYIFkWHyzKRC20SKs5cFgyb7LYpxGKb8DCPc7N7tHFzi4128XbFi/siRkOByhy5bBq+95o8M+SEjw9Xtu+QSqFuXIUNctae2bXO8M2xUbdeMDIR9P9u5X6Rhys8wwokq3HknLFwIQ4a4xNN+cumlrmTSiy86p5toYuxYJ3OXLqi6THCJie74L1KQ8uVYWb4h1RbbuV+kYcrPMMLJG2/AsGHw0ktO8UQCb74JcXHw8MN+S3JsJCVB+fJw/fXMnOmiNyLJ5JnJnvMSOSdtEgumZZnH3/AJU36GES5+/dWlL2vbFh5/3G9pDlK9unN+GTkSfvjBb2lyx+bNLlNN+/ZQtOiB/N/XX++3YEdS8ebmFCGVxYP/9FsUIwBTfoYRDlatctuSU091Adl+OLgcjYcfdrJFi/PLxx9DWtohJs/LLoMKFfwW7Egq39CUdOJIH23nfpGEKT/DCDV797otSVqa262UKuW3REeS6fyyfLnLLxrJqDqT5wUXwBlnMHnywb8tIpJSpVhV6TxOXD4Wq7EdOZjyM4xQouoSVc+YAZ995vJqRiotWjgN8tJLTglGKhMnwqJF0KUL4HZ9CQlw7bU+y3UUUpok0nD/VGZP3OW3KIaHKT/DCCUffAAffQQ9esBVV/ktTc688UbkO78kJUHp0nDTTWRkOP+hVq2gTBm/Bcueqrc1J579LP/od79FMTxM+RlGqPjjD3joIWjd2jmURAPVqrmsM99951qksW2by4F6661QogQTJ8K6df6WL8oN5Vo3IVUSYJyd+0UKpvwMIxSsX++8OmvWhE8/ddHX0cJDD8Hpp7vXvXv9luZQPv/cOeQEmDyLFYOrr/ZZrpwoVoxVVS+gzuqxpKb6LYwBpvwMI/ikpjrFt2uXc3ApW9ZviY6NwoWd88uKFfDKK35Lc5BMR5eGDaFhQ9LTXQWo1q2hZEm/hcuZ9IsSqa8zmT5mm9+iGJjyM4zg88gj8OefLqThzDP9liZvJCbCLbfAyy+79GeRwLRpMHs23HUXAOPHw6ZNkW/yzKR6++YUQln1yQS/RTEw5WcYwWXwYHj/fXjssbDW1dm6leCnz3r9dbcLjBTnl6QkKF7cnffhTJ4lS8KVV/osVy4p1eI8UgoVI37CWL9FMTDlZxjBY/p0uOceFzLw0kthW3bePOen0r59kBVg1arO+eX77/13ftm9G774wv1BUaYMaWkuJ3ibNv7lBT9mihRhdY2mnLJuHHss05nvhFT5ichAEdkkIvMC+sqLyGgRWeK9lvP6RUT6iMhSEZkjIg0D7unojV8iIh1DKbNh5InNm10ge+XK7iEdHx+WZdPToXNnV9Xns89ccfag8uCDzvnlwQf9dX4ZMsQpQM/RZcwYt9uN2MD27LgkkbOYy+TvN/stScwT6p3fYKDVYX1PAr+qal3gV+8zwBVAXa91BT4ApyyBHkBj4DygR6bCNIyIID3dHTxt3Oi2IxUrhm3p3r1h6lSX7ev22+GZZ1wkQNAoXBjeew9WrnTnf36RlOSU8AUXAE4XlikDl1/un0h54YSOzQFY9/l4X+UwAFUNaQNqAvMCPi8CqnjvqwCLvPf9gFsOHwfcAvQL6D9kXHatePHiahhh4bHHVEF10KCwLvv336pFiqhee61qRobq3r2qTZqoFiumOnVqkBe79Va32JIlQZ44F8ye7b7f3r1V1f2cpUurduoUflHyTVqa7o4rpV9VvMdvSbIESNYQ64RIaX6c+VVW1fUA3mslr78asCZg3FqvL7v+IxCRriIyTUSmpVsSPSMcDBvmHEPuuw86dQrbshkZrixg8eLOv0YEihZ1kRWVKrmzsLVrg7jga6+5HGIPPhgCz5ocSEpya7dvD8CoUbBzZxSaPAHi4/mn1kWcsXkc27f7LUxsE0kOL1mludej9B/ZqdpfVRupaqP4MJ25GDHMvHnuwK1JE2d/DCPvvecSyLz1FlSpcrC/UiXnn7J7t1OAyclBWrBqVXjuOfjpJ1f6KFzs3euSBNxww4GSDUOHurctWoRPjGASd1lzTmURk0es81uUmMYP5bdRRKoAeK+bvP61wAkB46oD647Sbxj+sX07XHedq9Dw5ZduZxImVqyAJ5+EK644sBk6hDPPdGdis2e7c8CMjCAt/MADbvKHHiJs7opffeW+a8/RZc8ep3tvuMEdR0YjJ3RIBGDT0OhMdSYiZUXkKxH5W0QWisgF0ejI6IfyGwlk/qAdgW8D+jt4X9b5wA7PLDoKaCki5bwvtKXXZxj+kJHhtMrKle7hXLVq2JZWdTHecXHQr1/2ZQGvvNLlqP7mG+jePUiLZzq/rFoVvlCOpCSoUwcuuQRwtXaTk6PU5OmRcG59dsaXo8SUqI33exv4WVVPBeoDC4lGR8ZQHigCXwDrgTTcDu5OoALuy1nivZb3xgrwHrAMmAs0CpinM7DUa3fkZm1zeDFCRo8ezgHjvffCvnS/fm7pfv1yHpuRodq1qxs/eHAQhbj9dtWEhNA7vyxc6IR/+eUDXTfcoFq5smp6emiXDjWLTr9Wl1FLN270W5JDIQeHF6A0sAKQw/rD4sgYzOa7x02omik/IySMHOn+23Ts6LRLGFm9WrVUKdXmzXO/dGqqG1+4sOrvvwdJkPXrnbvlFVeE9jt47DHV+Hi3nqru3KlatKjq/feHbslwsfLRPqqg372zwm9RDiEXyq8BMAUXxjYTGACUALYfNm6b9/qnM+KJAAAgAElEQVQ90DSg/1egEfAY8HRA/zPAY0dbO9gtkhxeDCOyWbzYmTsbNnR1+rKzOYYAVbj7bhfMnpSU+6ULF3ZHkjVruiPKoNSoPf54eP555/zy7bc5j88LqamuDuLVV7v1cGd9KSnRk8vzaFS73Z37bfkq4s794jM95r3W9fDrQEPgA1U9G0jmoIkzK/LtyBgqTPkZRm7Yvdtpj4QEF8ge5pxan3zidM1LL8FJJx3bveXLOw/Q/fudLtmxIwgC3X8/nHVW6Jxfvv3WZc3xHF3AeXlWq+aca6Od+PpnsD2hImVmRNy5X7p6HvNe63/Y9bXAWlWd7H3+CqcMo86R0ZSfYeSEKtxxB/z9t3OjPPHEsC6/fr3TMRde6Bwu88LJJ8Pw4W7zevPNLilNvoiPd84vq1fDiy/mc7IsSEqCGjWgZUvAOXz+/LNL7RlNpRGzRYRNpyfSaNc4Vq8Kc9xkPlDVDcAaETnF62oBLCAaHRnDaWMNZ7MzPyNovPKKO+d79dWwL52R4TK4FC2qumhR/ufr39/9KP/5T/7nUlXV9u2d80swhMtk+XInZM+eB7oGDXJdkycHbxm/WfN0X1XQ4S8v9luUA5CLDC+4c79pwBzgG6AcYXJkDGbzXUmFqpnyM4LCL7+oFiqketNNYXdwUVUdOjT4eveRR9yc778fhMkynV9atgze99O9u/vOV68+0HX55aq1avnyKwgZ+xcuUgUdeH5fv0U5QG6UX0Fp4n7egkeJEiU0OWjpLYyYZOVKaNTIOVxMmhT2cuGbN7tczrVqudq4wUpatH8/XHONMyP+9BNcdlk+J+zTx9llhw93lS3yQ3q6Mys3aOCC+oB//3W/gsce8ze3dtBRZUuJE/iDplydPCSc/lPZIiJ7VLWE33KEg4JgPTeM4LN3r3uQp6e7hJlhVnzg0mju2OEKwgczW19cHHz+OZx2Gtx4ozvKzBf33Qf16rmit/n9g/PHH2HdukMcXb7+2insaA5szxIRtpyVSOO941i6pGBuQiKZHJWfiDwgIqW99/1EZIqIRGlWPcPIJY8+CjNnurySdeuGfflvvnG+Nc884zKKBZvSpV192iJF4KqrYMuWfEyW6fyyZk3+CwomJbltXuvWB7qGDHEOOw0a5G/qSKTMdc2pzCZmfrbAb1Fijtzs/Lqq6k4RaYmrpnAv8GpoxTIMHxk71sXxPfKI0wxhZts2uPdeqF/f5fAMFTVrOiW7dq3b5Kam5mOypk2hY0dX4WLx4rzNsXat2/ndcceBxJ0bNsBvv7ldXySYBYNNpZtdvF/ydxEX8lDgyY3yy9yPXwEMUtXpubzPMKKP3btdraC6deGFF3wRoVs3d943cGDokzdfcAF8+CFMmOAUbr5cAF55xdVY+s9/8jbRoEEHazV5fPWV6ypwJk8PqVWTzSVrUnH+uOAlIDdyRW6U2GwR+RG4GvhJREoS5kh8wwgbTzzhEjcPGuQe5GHm559h8GC342vYMMfhQeG22+Dpp52yfeONfExUubL7g+GXX9xB3bGQkeG0cIsWULv2ge6hQ+GMM1wrqGw/uzlNUsczb45pv7wgItVFZISIbBaRjSIyXESq53hjTu6gQBwu63Zm3MZxwNl+u6nm1CzUwThmxo51MQCPPOLL8jt2qJ5wguppp6mmpIR37f37Vdu2VRVR/fbbfEyUlqbaoIFq9eqqu3fn/r5Ro9x3/8UXB7pWr3ZdvXrlQ54oYHPvT1RBP310ht+iRGWoAzAauAOXei0e6ASMzum+HHd+qrofOAl31gdQDDN7GgWNTHNnnTq+mTufeAL++cdtOosUCe/ahQq5VJrnnAO33upqAeaJTOeXtWuP7XtMSnIVaq+77kDXl1+614Jq8szkuBvdud++n+zcL49UVNVBqprutcFAxZxuyo2357tAInC715UM9M2PpIYRcTz5pIvrGzjQF3Pn2LHQt6/zsWncOOzLA+7H/vZbKFvW5QDdsCGPEzVpAp06ORtqbuIoNm1yC3focIjWHzoUzj7bF2fb8FKtGhvKnEzVxePyn3YuNvlXRG4XkTiv3Q7k6L+cmx1cE1W9G0gBUNWtQPjKVhtGqBk/3u1WHnwQLroo7MsnJ7sCtXXquGIJflK1qguB2LLFBcLv3ZvHiY7F+eWjjyAt7ZDYvhUrYMqUglHBITfsPrc5TdInMGOKab880Bm4CdiAqx/b1us7KrlRfmkiUgjPyUVEKgB2MmsUDJKToXNn52SR3xi1PNK9u3vYf/ihL5vOIzj7bBfeOGWK+2ry5AFaqZL7PseMcS6b2aEKAwa4rN2nnXage+hQ93rTTXlYOwqpeFMipdnFwk+n+y1K1KGqq1W1japWVNVKqnqtqq7K6b7c5I14DxgOVBSR53Aa9rl8ymsYkcFTTzlz52+/QYnwZ3X64w+XHez++6FZs7Avny3XXefKJz31FJx6KvTokYdJ7rnHafRHHoErrsg6S86ECS4u8H//O6R76FBn/q1ZM0/iRx1lrrkEusL+0WMBn+zeUYaIPHuUy6qqvY52f7Y7PxH5UURqqurHwNPA68A24EZVHZInaQ0jkvjtN3jnHWea88HcuXev21nVqBGZOSufeMIdw/XseXAndkzExTlz8j//ZO/8kpQEZcq4PGseixfDrFkF39HlECpVYl2FM6mxfBz79vktTNSQnEUDuBN4Ise7j+I+ehOwGOgOFPbbnfVYm4U6GEdl927Vk05y7Vhc8oPIE084V/7Ro31ZPlekpKg2bepKKuW5nFDnzqrx8aoLFhzav2WLapEiqvfdd0j388+7kIu1a/O4XpSy7KoHNZli+tvofb7JQBSGOjixKYXbpK0AXgEq5XRPtjs/VR0GnA2UBqaJyGMi0i2z5UVNG0bE8L//wfLlzrvTB3Pn1Knw2mvO0eXSS8O+fK4pUsTFq1epAm3auNq1x8zLLzuT5+HOL59+Cvv2HeLoAi6XZ9Omrmp7LFG5XSLF2cuSTyfnPNgAQETKi8gLuNqC8UBDVX1CVTflcGuODi9puK1kEZxmDWx5FfYUEZkV0HaKyMMi0lNE/gnovzLgnqdEZKmILBKRy/O6tmEA7pypTx/3ML744rAvn5rqzJ1VqrhUmJFOxYrw/ffOTNumjQuJPOYJXnwRfv31YPCeqjN5Nmp0SMbqefNgwYIYM3l6lLjyYjIQGDfOb1GiAhF5DZgK7ALOUtWeqrot1xMcZRvZClee/mWgeIi2qnE499QTgZ7AY1mMOR2YjVPAtXAVgeNymtvMnkaWJCer1q7tq7nz2WedufO773xZPs/89JOrMdumjWp6+jHenJ6u2rChatWqqjt3qv71l/sS+vU7ZNjTT7s1NmwIntzRxJrKDXU8F/v1TzOqzJ64qIO9nvLbGdB2ATtzuv9oO7/uOOeWJ1V1T6616bHRAlimR3dLvQYYoqr7VHUFruT9eSGSxyjo/O9/sGyZ80L0wdw5e7bbBN1+uy8FI/JFq1bw1lswcqTzAj0m4uLg/fddrb5evdyur3jxQwL5VJ3JMzHRpQmNRdIvas75/MWfv+Y1wDJ2UNVCqlpMVUupaumAVkpVS+d0/9HO/C5S1fnBFfcI2gFfBHx+QETmiMhAESnn9VUD1gSMWev1Gcax8fvvB+MKLrkk7MunpblqPeXLOyUSjTzwgKv+8NprLg3bMdG4sUsh17s3fPGFU3ylDz6jZs6EpUtj0+SZyfG3JFKEVFZ8/qffohR4fMvRKSIJQBvAOwTgA6A20AAXpZ+ZXz6rKl5Zht2KSFcRmSYi09ItT5ARyJ497qCtZk3f4gpef9094N9/36WxjEZE4O23nZPO3Xe7aJFj4qWXoFQpd4B4mKPL0KEuNej11wdP3mij6GUXkU4ccRPs3C/U+Jmg+gpghqpuBFDVjaq6X1UzgCQOmjbXAicE3FcdWJfVhKraX1UbqWqj+PjcxO8bMcPTT7ttxYcfZh1sHWIWLnTxcm3bwg03hH35oFK4sPNbqV3bKaqlS4/h5ooVncmzS5dDkpiqOuV36aXR+4dBUChVivXVz+W09WPZlnvXDSMP+Kn8biHA5CkiVQKuXQfM896PBNqJSBERqQXUBaaETUoj+pk40dkZ77vPHSiFmf373aazVCl4992wLx8SypZ1OUDBJcHevv0Ybr7hBujf/5DS7JMnuzKKsZLL82joJYmcy1T+HLXLb1EKNL4oPxEpDlwGBFa8fFVE5orIHFwViUcAvHPHYTjP05+B+9WVWTKMnMk0d554oku27AN9+sCkSe61IDly1KnjYgCXLXM5OPNz0jB0KCQkwLXXBk++aKXKrc0pTDqrP5/otygFGlHNS9bayKdEiRKanJyc80CjYPPoo/Dmmy7GrHnzsC+/dCnUq+fMed9+e8hmp8AwcKDzY7nvPpfN7FjJyHAp3s45x31HMc+ePaSVLMsn5R+m87+vhnVpEdmjquF3g/YBK0prFFz+/NN5Ft57ry+KLyPDKYWEBPjgg4Kp+MBtrB97zDny5MWs+8cfLv2nmTw9ihdnfc0LqLdlLBs3+i1MwcWUn1Ew2bvXxRXUqOGbubNvX5dM5s03C36qrpdfdtlfHnoIRo06tnuHDIFixdzZoeGIa5HI2cxk4nfm9RIqTPkZBZNnnnHlAT780HmahJmVK+G//4XLLnM6uKATFweffQZnneXO/xYsyN196emu3F/r1r444UYslW9tThwZbBg2wW9RCiym/IyCx59/uu3W3XdDixZhX14VunZ1Zs6kpIJr7jyckiVd9pdixVz2ms2bc77nt99g06bYDmzPivgmjdlXqCjFp1i8X6gw5WcULDKL5J1wgktD4gODBsHo0c7aeuKJvojgGzVqOKeVdetcDGBOtemGDnVK88orjz4u5ihShI11mnLOjrGsyrEmuZEXTPkZBYsePWDRIt/Mnf/8A926uWIR99wT9uUjgsaNYfBgF155992HVjEKJC0Nhg93Z4XFi4dVxKgg4fJE6jGXP7/NxRY6zIhInIjMFJHvvc+1RGSyiCwRkaFeBi+8+OyhXlWeySJSM2AOX6v1mPIzCg6TJsEbbzibow9F8lSdwktNhQEDoFAM/+9q1879HfLRR/BqNt76Y8bA1q1m8syOyrc4D+V/hx9rDrmw8BCwMODzK0BvVa0LbMNVU8d73aaqdYDe3jhE5HRcbuczcBWE3heRuDDJDpjyMwoKe/dCp05Qvbpv5s4vvnB17/7v/1wAeKzTo4dTbE89Bd98c+T1oUOhTBm43Cp0Zok0Ooe98SUpPW1strtnPxCR6kBrYID3WYDmwFfekI+AzHQF13if8a638Mb7Xq3HlJ9RMOjZ05k7Bww4pFJAuNi40dXGPf98ePDBsC8fkYi4889zz4XbbnNJvTNJSYERI+C661y1eCMLChdm86nNOG/POBYvDtuq8ZnFAbzWNYsxbwH/xdXTA6gAbFfVzBw/gZV3DlTl8a7v8Mb7Xq3HlJ8R/Uye7EomdOniYgt84IEHXIXzgQOd27/hKFbMOcBUqODi+NZ5KelHjYKdO83kmRPFWydyGn8z6essc/mHgvTM4gBe6x94UUSuAjap6vTA7izm0Ryu5bpaT6gw5WdENykpztxZrZpTgD7w1Veu9ewJp53miwgRzfHHuyTY27fDNde4dKtDhzqF6EMkSlRRoa1LxL79m/H+CnKQC4E2IrISGIIzd74FlBWRzFI6gZV3DlTl8a6XAbZyDNV6QoUpPyO66dkT/v7bBdT5YO7cssXVxm3Y0KX4MrKmfn34/HOYPt1VsR850hV3KFzYb8kiGzm7AckJZakweywZGTmPDzWq+pSqVlfVmjiHlbGqehswDmjrDesIZGZpHel9xrs+Vl1Cad+r9Zjyi2YmTnQxbWvX+i2JP0yZ4pxb7rrLN6+Jhx92HosDB9qDPCfatHGxjyNGQHKymTxzRVwcW868mAv2jWPuXL+FOSpPAN1EZCnuTO9Dr/9DoILX3w14EiKjWo9VdYhW9uyB0093RdDKlXM7n2ivknospKS4MgA7d8K8ec5tMMx8/707x3r2WXjuubAvH5WoOoegiRNh2jQ7H80N257rQ7meDzHg6ZXc1Su0WROsqoMR+fTq5RTfwIGupHbbtm4HtHu335KFh+eecwkkk5J8UXzbt7sA7jPPhO7dw7581CIC77wDM2aY4sst5a535367v7NUZ8HElF80Mn++c+7o1MllTf7zTxdMNXCgO3yaOtVvCUPL1KkucrpzZ2jVyhcRHn8cNmxwrvwJCb6IENXESr7ToHDGGewqWpHK88fmq2CwcSim/KKNjAyXRqR06YPB3IULw4svwrhxzhzYpAm89BLsL4AF7/ftc0q/ShWXzcUHRo924YSPPw6NGvkighFLFCrE9gaX0DR9HNOmFsxjKj8w5RdtZCZNfO01OO64Q69dfDHMnu0yCv/vf86PfM2aLKeJWp5//qC5s2zZsC+/a5cLJzzlFJfBxDDCQdnrm3MCa5n55VK/RSkwmPKLJv791203mjZ1u5+sKFfOVQcdPNj5lderB8OGhVPK0DFtmnMXvOMOuOIKX0R46ilYvdrlzS5WzBcRjBikVBt37pfyk537BQtTftHEf//rvBs/+ODoWZNFoGNHl0/q5JOdT/kdd7htS7SSae6sXNnV6vOBCRPgvfect+KFF/oighGrnHwy20tUpfrisaSk+C1MwcCUX7QwYYLzrnj0UedimBvq1HEm0qefho8/hrPPdqnAopFevZyjj0/mzj174M47oVYtl7jaMMKKCLsbJXJRxngm/WXnfsHAN+UnIitFZK6IzBKRaV5feREZ7dWEGi0i5bx+EZE+Xu2nOSLS0C+5fSE1Fe6911VGfeaZY7u3cGGnOMaPdwXULrzQPb2jyRlm+nR4+WW38/Op6umzz8LSpc7cWSImoqCMSKN82+Ycz0bmDluY82AjR/ze+SWqagNVzfSZexL41asJ9av3GeAKXPqbukBX4IOwS+onb77pnDzefTfvT96LLnLOMDfe6HaCiYlERYno1FTfzZ2TJkHv3s7JNjHRFxEMg+Kt3T++tF/G+ixJwcBv5Xc4gbWfDq8J9bE6JuGSqFbxQ8Cws2KF83C87jq46qr8zVW2rEuw+PHHMGuWS7g4ZEhw5AwVvXq5DC79+ztnnjCzb58LJ6xWzfnaGIZv1KrF1jI1qbliXMzksgglfio/BX4RkekBNaMqq+p6AO+1ktfve+0nX1B1tXIKFYK33w7OnCLQvr1TfqedBrfc4pxjdu4MzvzBZMYMF6/YoQO0bu2LCM8/DwsXOt3rQ95swziElPMTuVjHM3FCBGS5jnL8VH4XqmpDnEnzfhFpdpSxuar9JCJdM4swpheEVAgjRsCPP7on8Akn5Dz+WDjpJPj9dxes9umnzhlm0qTgrpEfMs2dlSrBW2/5IsKMGW6316mTb4lkDOMQKrRNpAJbWTh0jt+iRD2+KT9VXee9bgJG4ErYb8w0Z3qvm7zhuar9pKr9M4swxsfHH345uti1y/nU168futLg8fGuJNCECS5zTNOmTtFGwh8O//d/MHcu9Ovni7kzNdVFh1Ss6NtRo2EcQZFW7txPf7Vzv/zii/ITkRIiUirzPdASmMehtZ8OrwnVwfP6PB/YkWkeLbA8+6wre92vn1NSoeTCC50ZtF07txO85BJYuTK0ax6NmTNdurb27V3ZhDCzd6/LET5nDvTt64vuNYysqV6dLRXqUvefcWzd6rcwUY6qhr0BJwGzvTYf6O71V8B5eS7xXst7/QK8BywD5gKNclqjePHiGrXMmKFaqJDqPfeEf+1PP1UtXdq1zz4L//r79qnWq6d6/PGqW7aEffnly1XPPlsVVHv2DPvyhpEj6665W3dQSkd8mRb0uYFk9UEn+NF8FyBULWqVX3q66rnnqlaqpLp1qz8yLF+u2qSJ++dx222q27eHb+0ePdy6I0eGb02PH35QLVdOtWxZ1e+/D/vyhpEr0j4dogr62o2Tgz53LCm/SAt1MPr3dyV73nzTP3tbrVrw22+uZt6QIdCgAfzxR+jXnTXLnfXdfntYzZ379ztr71VXuTwC06f75lxqGDkSf+klAMT9Zud++cEquUcSGzbAqae6CuVjxkRG0bO//oLbbnMB8c884wLkQ3EGmZoK553nvoMFC6B8+eCvkQVbtrgfb9Qo59X5/vuWsNqIfDYffyYzNlaj/vpRHH988Oa1Su6GPzz6qPO2eP/9XCm+6dPhySddsYeQccEFbkd2221uJ9isGSxfHvx1XnrJZaDp1y9sim/aNPd3xrhxbtmBA03xGdFBxiXNacpEfhud6rcoUYspv0hhzBiXfeXJJ12xuBxYuhQuv9zFoZ1+Onz5ZQhlK13aZYX5/HO3K2vQAD75xAXhB4PZs+GFF+DWW+Gaa4IzZw4MGOCcXFVd7u+uXSNjo20YueG4GxMpwR5WDpvityhRiym/SCAlBe67D2rXdgXjcmDLloP5nb/91p1T3XQT3HCDsxqGjFtucYqqfn2XdeW222D79vzNmZbm7I0VKkCfPkER82js3euqM3Tp4iI6pk+Hc88N+bKGEVTiEi8mAyFhop375Rm/PW5C1aLK27NnT+fh+MsvOQ7du1e1aVPVIkVU//jD9aWlqb7yiusrV071o49UMzJCKG96uuoLL6jGxameeKLq77/nfa7nnnM/+4gRQRMvOwLDGJ5+2v0YhhGtbKx+to7lEl2+PHhzEkPenr4LEKoWNcpv8WLVhATVW27Jcej+/W4YqA4deuT1v/9WvfBCd/2KK1RXrw6BvIFMmqRau7aLSXz6adXU1GO7f9Ys1fj4XP3s+cXCGIyCxuaOj+peiujgD/YEbU5TfgWgRYXyy8hQbdFCtUwZ1fXrcxzevbv7jb38cvZj9u9X7dNHtXhx1VKlVPv2dX0hY+dO1U6dnGCNG6suXZq7+1JT3TasUiXVzZtDJl56uuqzz6qKqDZooLpsWciWMoywkvHd96qg/9fi16DNacqvALSoUH6ffeZ+Be+9l+PQDz90Q7t0yZ1Jc9ky1ebN3T2JibnXSXlm6FC3rSpZUnXw4JyF7NXLCff11yET6d9/VS+/3C3TqZPqnuD9gWwY/rNjh6ZLnL5Z8umgHXOY8isALeKV39atbtdz7rk5Hj6NHu2sgy1bHptlMSNDtX9/l6msWDHV3r1DfM61apVqs2bun9XNN2efoWbOHNXChVXbtQuZKFOnuuPIhATVfv1CfAZqGD6xoVZjnUgTXbgwOPOZ8isALeKV3733urOy6dOPOmzuXKe8zjpLdceOvC21Zo1q69but33BBaoLFuRtnlyRnq764otOW59wgur48YdeT01VbdhQtWLFkJk7k5Kc0qtRQ3XKlJAsYRgRwda7n9RU4rXfG7uCMl8sKT8LdfCDyZNduYD//AcaNsx22Pr1Ls1WiRLwww95L6ZavTp8950r27dokQvTe+klF2UQdOLiXLjGn39CkSKQmAjdux9c7NVXXaG8Dz6A444L6tIWxmDEGmWvb05h0tn09US/RYk+/Na+oWoRu/NLS3OeF1WrHnUrt3u36jnnqJYo4Yo8BIsNG1TbtnW7wLPPVp05M3hzH8GuXaqdO7vFzj3XhTMULuxMokEmMIzhmWcsjMGIEZKTNa1QYX276H+D4thGDjs/XF3VccBCXEWeh7z+8sBoXEWe0UA5r1+APsBSYA7QMGCujt74JUDHo60biua7kgpVi1jl17u3+9q//DLbIenpqldf7ayioXLL/+or1cqVnXXymWdUU1JCs46qup+1XDn3c1esqLppU1CntzAGI5bZcMpFOoVGQfkjORfKr0qmAgNKAYuB04FXgSe9/ieBV7z3VwI/eUrwfGCyHlSWy73Xct77ckdbO9jNdyUVqhaRym/NGucNecUVR/XAePBB95t5993QirNli2qHDm6tM85QnRz8CikHWbNGtX37XAXy55bMMAawMAYjdtnx8LOaTiF9p9e2fM+Vk/I7vOEKjl8GLAKq6EEFuch73w+4JWD8Iu/6LUC/gP5DxoWj2ZlfOHn4YUhPh3ffzTaR5Ntvuyxf3brB/feHVpzy5eGjj9x54o4dLof144+7s7OgU726yw962WVBmW7LFnce+vzzLjvan3/CSScFZWrDiCpKX9ucODLY+s2EsK4rIjWBs4HJQGVVXQ/gvVbyhlUD1gTcttbry64/bJjyCxc//ADDh8Ozz2b7lP72W3jkEbjuOnjttfCJduWVMH++cxR5/XWXuvP338O3/rFi1RgMI4Dzzyc1rijl54wLhhNbvIhMC2hdsxokIiWB4cDDqrrzKPNl9Ve+HqU/bJjyCwd79sADD8Bpp7myRVkwbZoranDuuc4rs1CYfzOlSzsH1F9/dZvTZs2cyLt2hVeOnLBqDIZxGEWKsO30C7kobSzTpuV7tnRVbRTQ+h8+QEQK4xTfZ6r6tde9UUSqeNerAJu8/rU4J5lMqgPrjtIfNkz5hYNevWDlSqddEhKOuLxqlasiXqkSjBwJxYuHX8RMmjeHuXPhoYdcWcGzzoLRo/2TJxMLYzCM7Cl5VSL1mcNf34WyuCeIiAAfAgtV9c2ASyNx3pt4r98G9HcQx/nADs8sOgpoKSLlRKQc0NLrCx/hPGAMZ4sYh5d585xLZadOWV7ets05m5QtG+Lg8zzwxx+qp5ziHEo6d3ay+oGFMRhGDvz5pypozzOz9yLPDeTs7dkUZ56cA8zy2pVABeBXXNjCr0B5b7wA7wHLgLlAo4C5OuNCIJYCdxxt3VA08YQocJQoUUKTk5P9FSIjAy6+2BWA/ftvqFjxkMupqe68bcIEGDXKxYNHGikproD7a6+5nWnfvtCmTfjW//FHuP12Z+b89FPn5GIYxmGkpbGvRDkG7e9Ip+T3KFo0b9OIyB5VLRFc4SKTsJs9ReQEERknIgtFZL6IPOT19xSRf0RklteuDPvCTBwAAB5PSURBVLjnKRFZKiKLROTycMucZz76yB1MvfrqEYpPFe65x52xJSVFpuIDKFrUZYOZPNn9CNdc484mN28O7br790OPHk7ZnXiiM3Oa4jOMbChcmB31mtEsYxx//eW3MFFCuLeaZB8k2RN4LIvxpwOzgSJALdz2OS6ndXw3e27erFqhgiuwl0XqhRdecGa8Z5/1QbY8sm+f6vPPuyQtxx2nOmRIaBJGWzUGwzh29j7/qiroSw+uy/McWG7PkCrb9ao6w3u/C5cm52jxHdcAQ1R1n6quwNmHzwu9pPnkiSdc8NwHHxzhuvn55/D009C+PfTs6Y94eSEhAZ55xqXmrFUL2rVzYRnr1wdvDQtjMIy8UfTK5gDs/Wm8v4JECb56ex4WJAnwgIjMEZGBngcQHEMwpIh0zYxPSU9PD5HUueD3391Tu1s35y552KU77nBHgUlJ0emmf+aZLqj8tdfcWeXpp8Pgwc6Umx8sjMEw8kGDBuwtWpaGO8ayf7/fwkQ+vim/LIIkPwBqAw2A9cAbmUOzuD3Lx6yq9lcvPiU+Pj4EUueC1FS49153UPXss4dcWrwYrr3W7ZpGjHBFD6KV+Hh47DGYPdvp9zvugCuugNWrj30uC2MwjCAQF0fRls24ptQ44uL8Fiby8UX5ZRUkqaobVXW/qmYASRw0bfoeDHlM9O7t0qW8+66rReSxebPz7IyLcx6M5codZY4o4uSTYfx49+NOnAhnnOEsvRkZubt/xQq32xs40JlUf/wx6JWODCNmkM6dncnEtn45EvZQBy9I8iNgq6o+HNBfRb3ccCLyCNBYVduJyBnA5zhlWBUXQ1JXVY/62/Ul1GHFCvf0b9UKvv76QPfevdCiBcyc6c6yzj8/vGKFi5Ur3e5tzBhn1h0wAOrUyX68hTEYRmRhoQ6h5UKgPdD8sLCGV0VkrojMARKBRwBUdT4wDFgA/Azcn5Pi8wVVlw+sUCGXndojIwM6doRJk9wDvqAqPoCaNeGXX5zSmzUL6tWDN9888o9QC2MwDMN3/HY3DVULe6jD8OHON/+NNw7pfuIJ1/3aa+EVx2/WrnU1CUG1cWPV+fNdv4UxGEbkQgyFOliGl2Cwa5dLWn3ccc5X33O26d8f7r7bBbO//37seS6qwpAh8J//uK/ooYdg2DAXGvHOO85EGmvfiWFEMrFk9jTlFwy6dYO33nL+/55d8+efXbLqli1dsmq/nE8jgU2bnAIcNgxq1ICvvjJvTsOIREz5FQDCpvxmzoRGjdw2pm9fAObMgaZNoXZtl7ezVKnQixENTJoEp5xScDxdDaOgYcqvABAW5bd/PzRp4rw8Fy2CcuX45x+3+VN1+TCrhbU2sWEYRt6JJeUXw8a4IJCUBFOmwCefQLly7NrlTJ3bt7uYN1N8hmEYkYnt/PLKxo3OhnfOOTBmDOn7hWuucem+vvvOZTsxDMOIJmznZ+RMt24uev3991GEhx5yQdt9+5riMwzDiHR8TWwdtYwZ40ozPPkknHIKvXu7UIbHH3ehDYZhGEZkY2bPYyUlxaUuyciAefMY/kNRbrwRbrgBhg49onqRYRhG1GBmTyN7XnkFliyBUaOYPLsot98OjRvDxx+b4jMMw4gWbOd3LCxZ4orZXX89K178gsaNXQzfpElQsWJwlzIMwwg3sbTzs71KblGF++6DokXZ/uybXHklpKc7JxdTfIZhGNGFmT1zy5AhLqThrXe57r4qLFsGo0e7aAfDMAwjujCzZ27Yvh1OPRU94QQ6nTKJjz+L45NPXC06wzCMgoKZPY1D6d4dNm+mf8N+fPxZHM8/b4rPMAwjmrGdX05MmQLnn8+Cyx7kjF/eolMnGDjQSvEYhlHwiKWdnym/o5GeDueey761m6i6fSENmpXmp58gISE4MhqGYUQSsaT8zOHlaLz7LsyaRdfiX1K5bmmGDzfFZxiGURCwnV92rF1Lxqmn8VvGRbQr+QOTpwg1awZNPMMwjIgjlnZ+5vCSDen/eZjUPencn/Eu3/9gis8wDKMgETXKT0RaicgiEVkqIk+Gcq2M734g/pvh9NJneHHISZx7bihXMwzDiB7C+SwOJVFh9hSROGAxcBmwFpgK3KKqC7K7J89mzz172FrlDDbsLMboV2fx0ON2yGcYRmyQk9kzL8/iSCVadn7nAUtVdbmqpgJDgGtCsdD0616g/M6VjL7uAx58zBSfYRhGAGF7FoeaaFF+1YA1AZ/Xen1BZeuybZz8yzv8Wr0jD3x5scXyGYZhHEpYnsXhIFpCHbJSQ0fYa0WkK9AVICEPMQnla5dj4bfTaXxWOeLijvl2wzCMaCdeRKYFfO6vqv0DPufqWRwNRIvyWwucEPC5OrDu8EHeL6k/uDO/vCx0WpuT83KbYRhGQSBdVRsd5XqunsXRQLSYPacCdUWklogkAO2AkT7LZBiGEWsUmGdxVOz8VDVdRB4ARgFxwEBVne+zWIZhGDFFQXoWR0WoQ14ISSV3wzCMAoxleDEMwzCMAowpP8MwDCPmMOVnGIZhxBym/AzDMIyYo8A6vIhIBrA3j7fHA+lBFCeURJOsEF3yRpOsEF3yRpOsEF3y5kfWYqoaE5uiAqv88oOITMsh0DNiiCZZIbrkjSZZIbrkjSZZIbrkjSZZ/SQmNLxhGIZhBGLKzzAMw4g5TPllTf+ch/x/e3ceJHdVrnH8+yQEgUhAIbigQKDYArKEi4BgVOSCC3ARAUFUFqvc2VS87ihquSMKLngRAgqIIEvixhoIm0sSCCDoRYOooHiRLUbEAM/945zONJPunkEnfc4v/X6qpmb615OqpyYzffqc3znvW40mZYVm5W1SVmhW3iZlhWblbVLWYuKeXwghhIETM78QQggDJwa/EEIIAycGvxBCCAMnBr82klaVtGnpHCOR1Kg+85KeWTrDaDUpawjhXxcbXjJJewFfAFa2PUXSNsDxtvcuHG0Zku4EzgdOt31b6TwjkXQHcBNwOvBjV/xL15Sskvbt9bztC/qVZbQkbQJ8HXiW7S0lbQXsbfuThaN1JelAYCPbn5L0fGAd2/NK5xpO0tOA1wIb0Nan1fbxpTLVLga/TNI8YFfgKtvb5ms3296qbLJlSVqd1EH5MNLs/TTgu7YfLhqsC0kCdgMOB14InAvMsP2/RYN10JSskk7v8bRtH963MKMk6WrgWOCUtr+xW21vWTZZZ5JOBiYA021vnlcFLrG9feFoy5D0E+AhYB7weOu67S8WC1W5GPwyST+zvYOkG2sf/NpJmg6cA6xJmg1+wvZvyqbqTtLLgO8AE4EFwPtt31A2VWdNytoEkn5he/thf2M32d6mdLZOJM23PW1Y3gW2ty6dbbia30TUaqWRv2Vg3Crp9cB4SRsDRwLXF87UUb7n92rSzG8D4IvAWcCLgR8BmxQL14GktYA3AG8E7gWOAGYC2wDnAVPKpXuyJmUFkLQGcBwwPV+6mrRc/1C5VF3dJ2kjwACS9gP+VDZST0skjWMo71rAE2UjdXW9pBfYvqV0kKaIwW/IEcCHgEeBs4FLgE8UTdTdHcBs4PO22wfo8/NMsDY3AN8G9rH9x7brcyV9o1CmbpqUFdKS963AAfnxG0n3K3veEyzknaTqI5tJuhu4k/RGo1ZfBb4PTJb0cdLP+ONlI3W1C3Bo3g/wKCDS8nfVK1clxbJnJml/2+eNdK0Gkp5u+2+lc4yWJNW6cWS4JmWFzsuGNS8lAkiaCIyzvah0lpFI2oJ0D1jA5bZvLRypI0nrd7pu+65+Z2mKOOow5AOjvFZc+8AnqaqNGO0kvUvS2rYtaSNJcyQ9KOlnkl5QOl87SeMkHQ7MkrRA0jxJ35X00tLZRvCIpF1aDyTtzL/ex3K5knSUpEnA34EvSZovaffSuUawOvCA7ROBP0lar3SgLt5Mut1xn+27Wh+lQ9Vs4Gd+kl4JvIq0pHFu21OTgKm2X1gkWAeSFpHuP6jt8mqkFxPbnlQkWBeSfml7i/z1D4FTbV+YB5RP2d65aMA2effkXcDlwH7Aw8A1wH8DF9s+qWC8rvKRnDOANUi/F/cDh9i+uWiwDlqbRSTtQVoC/QjpuM60wtE6kvRhYGfSUYdNJK0LnGt7lxH+ad/lN267ADsBi0i/u3NsX1w0WMVi8JO2Jm1mOB74aNtTi4DZth8oEqwDSSeRXuSOtX1vvnan7ao2YbRI+rXtTfPXv2jfIl7bTtrheST91PaO+fzUTbY3LxhvRHlGRa3HXWDoZyzpy6QjRRe276SsjaSbgG2B+U3ZAS7p2aQ38u8FnmF79cKRqjXwG15sLwAWSDrb9pLSeXqxfYSk7YBzJF0EnEzeiVap8yXNIL2xuFDS0cAFwMuB35cM1sESSRvZ/q2kacA/AWw/Kqnan/Hw3Z75LF2tuz3nSbqUtGP2A/m8aq27JwEezUv2rd2eq5UO1I2kU4GppB3K15BWL+YXDVW5gR/82mwg6dOkX6BVWhdtb1gu0rJsz5O0G/Au0rb2VUb4J8XY/pCkQ0nnEDcCnga8BbgIOLhgtE6OBWZL+gfpYPOBAJImAz8oGWwEjdjtmYsHfBSYDCy0/fd8dOCwssl6ukDSV4E1JB1Guq92WuFM3awFjAceJC1932f7sbKR6jbwy54tkq4lvYP+ErAX6Y9Sto8rGqwHSc8BtrX9o9JZVgT5BXot2/eVzjJaTdrtKWme7e1K53gq8p6A3Un3Uy+x/ePCkXqStDmwB3AMMN728wpHqlbM/IasavuKvNX9LuBjkq4hDYhVsv0n8iFhSc+2/efCkUatxrz5iMMyA1+NWds8ImkX29dC3bs9gZ9K2t72L0oHGUkuJPEj23sAVQ94AJL2JBW5mA48A7iStPwZuojBb8g/cjWHOyS9C7gbWKdwpqfiW6SqL03RpLw1Z30bcGa+99fa7Xlo0UTdvQx4q6S7gMVUfBDb9uOS/ilpUs2biNq8EpgDfNn2PaXDNEEse2aStgduJ9XI/ATpqMPnbf+0aLAQRqEhuz0bdRBb0jnAjsClpMEaANvvLhaqB0nPAlo7qn9u+y8l89QuZn4sXeI4wPaxwN+o+yY8knYEftmqkJF3zU21/bOyyTprUt4mZYVlW9mk25bVtrJp2jvty/NH9STtT2rJdhVpRn2SpGNtn180WMVi5pdJuhJ4eRNKW0m6EZjWypqXa+dWfFi4MXmblBWa1cpG0i0MFWlYhXTk4detQgjhXydpAfCfrdle3qV8uSvsQFGLmPkNuRG4WNJ5PHmJo7qmoKQ3LUsHadtPSKr5/7JJeZuUFeB5tl9ROsRo2H5SSbt8nvKtheKMKL8RGv5m+CFgLvBp2/f3P1VX44Ytc/6VKF/ZU81/1P32TNIvzK5t10w6lF2bhZKOJHXFBngHsLBgnpE0KW+TskKDW9nYnp/vtdfqMtIs9ez8+EDSofxFwAxg7zKxOvqJpEtIZ2oBXkdqbxa6iGXPBpK0DvAV0kBt4Arg6FpvcDcpb1Oyti0hrgRsTBqgq25lI6l9o8g4YBrpXOUehSL1JOna4XU8W9ck3TJ8JluapNeSapGKVNfzwsKRqhaDXyZpE9K7/WfZ3lLSVsDetj9ZOFoIy+i2c7Klxh2UktrPzD4G/A74vu1/lEnUW76PdrjtefnxNGBGrk9abU3SMDox+GW5JuKxwCltRWxvtb1l2WRDJL3P9udygetl/uNsH1kgVldNytukrO2atjsVlma0K+9JmX+2p5HK3YlU7/XNwM2kN8bn9PjnfSVpX+CzpLPJYmgFoKpOLzWJe35DVrP989ZW8ay22ni3589zi6YYvSblbVLWdl8nLR+2LO5wrQqStgS+Tbq/jqT7SO2XqmwQm8/4Ts01SDWs7F01A1/2OWAv27eP+J0BiMGv3X2SNiK/65e0H7l0WC1sz8qfz2hdy1vxn17j4eYm5W1S1mGatDv1m8C7bc8GUOrr+E3gRSVDdZOPC3wSWNf2npKmAi+0PaNsso7ujYHvqYmtsEPeCZwCbCbpbuBoUumo6kg6W9IkSROB24BfSzq2dK5umpS3SVmzhZKOlDQhfxxFvbtTJ7YGPgDbVwETy8UZ0QxS55Tn58d3AO8plqa3uZLOlXSQpH1bH6VD1SwGvyG2vRup5cpmeZdXrT+fqXk2sg9pO/N6pFY2tWpS3iZlhfQG7UWkWrR/BHYgtY2q0UJJH5G0Qf74MHBn6VA9rGP7bHLPQad+n4/3/ifFTAL+TupAsVf+2LNoosrVujxSwvdJlT0Wt107H6ixBcsESRNIL9An216iihuu0qy8TcpKPoJxYOkco3Q48HGGzs7Ooe5SgoslPZOhWyHbk8741eg9ww/dS5pSKkwTDPzgJ2kzYAtSw8r2ZYJJ1Nso9hTSNvEFwJy87b3m+1JNytukrEj6HOm+1CPAT4CtSecSv1M0WAe2HwCq3DXbxXuBWcCGeTf4uqQO6TWaJemVrfvTSn39zgOq2a1em4E/6iDpv0jv8vcGZrY9tQj4ru3riwR7iiSt5AZ1bm5S3pqzKjeulfQa0u/xMcDsGms6SroM2N/2g/nxM0h/Y1UecgeQtDKwOenowG22/1k4UkeSXg28j9R6a1PgTOBg2zcVDVaxgZ/52b6YVNNzJ9s3lM4zWvmXfQuePDutsZI/0Ky8TcpKOoMG8CrgHNv3DzuuU5O1WwMfpJlgrqhTFUndypatJwnbM7s8X4ztH+bl+kuB1YF9bN9ROFbVBn7wax1uBl4v6aDhz9d4uFnSN4DVSM1BTyUtxfy8aKgempS3SVmzWZJ+RVr2fEfenl9lxRTgCUnr2f49LK1SU+PS0/7589qkzUSzSTO/l5B2f1Yz+HUoyjCJtNv3iDxQV/f6VYtY9pT2sj1L0iGdnm8/91ULSTfnEkutz08HLrC9e+lsnTQpb5OytuTlw4eduo9PBFa3/efSuYaT9ArSub6r86XpwFtsX1IuVXeSZgJvt313frwu8BXbry2bbEi3162WGl+/ajHwM79Oh5sb4JH8+e+SnkvqRlHzzq4m5W1SVmDpRpLW14tz+bDq2P5Jro+5I2kmdcywqim12bA18GX3kO6nVaNhr1tVGfjBT9Iseiy92K6pbUnLDyStSSppNC9fO7VgnpE0KW+TsnbzLdLGh+rkwe4HpXOM0hxJPySVMjPpSMmcspE6k7Qz8DFgfdLrequ254Ylc9Uslj2ll/R63vbVvZ4vQdKqwNuBF5P+KK8Bvl5xdfzG5G1S1hWBpPm2q6tDCqC0c2g/0vIspIHvfFf4opnv+x5DesO29CC+7b8WC1W5gR/8mkjS90hHMVpnuQ4C1rR9QLlU3TUpb5OytkgaDzyLtpWc1qaSMBgk/cz2DqVzNMnAD34aagraketsCrpg+DmuTtdq0aS8TcoKIOkI4DjgXnIZLiptZrsikPQ12+8onWM4SZ8BxpOq5zzaum57frFQlRv4e340s/7djZJ2zC1XkLQDcF3hTL00KW+TsgIcBWxa8/KWpEV0foPZxJ5zM0oH6KI162uVYxTpZ75rmTj1G/jBzxV2vO6mbZY6AXiTpN/nx+uTOhBUpUl5m5R1mD8AD5UO0YvtKnefjkTSlsN7Ddqu9cznVR2uDfay3ggGfvCTdK3tXTq8O63xXWnTZqlNytukrEh6d/5yIXBV3pXYvtx1QpFgo5CruiytnlPx/clv5PJmM4Cz26vTVOhvbV+vQvp9jv5+PQz8Pb8QmkjScb2et/3xfmUZrVw27IvAc4G/kGbVt9veomiwHiRtTOpGsT+p0s/pti8rm2pkkp4GzKy5bmppMfiFEPpC0gLSPajLbW8r6WXAQbZr7T8ILN1Nuw/wFVKHDwEftH1Bz39YUK7683PbG5fOUquBX/YMocm6FGl4CJgLnFLZ+cQltv8qaZykcbZnS/ps6VDdSNqK1G/w1cBlwF625+fKPzcw1JewuGG71seTmnLXWoy9CjH4hdBsC0kvdOfkx68jHXvYBPgf6upC/2CulToHOEvSX4AqW0VlJ5N+hh+03Sp7h+17lLrQ16T9nvVjwL21tuGqRSx7htBgkubYnt7pmqRf1nQ/LRfd/gdp2fBgYA3grFqPaUg62vaJw64dZfvLpTKFsTOudIAQwr9lsqT1Wg/y12vnh1U1XrW92Pbjth+zfYbtr9Q68GVv6nDt0H6HCMtHLHuG0GzvAa6V9FvSjGoKqa/fRKCqiv+S9gU+C6xDylrjcSJyX8/XA1NyW6OW1UldPsIKIJY9Q2i4vK19M9Jg8qvKNrksJek3pE0jVZ8/y012pwCfBt7f9tQi4Oa4l7ZiiMEvhAaStKvtK/Nsahk1bsOXdJ3tnUvnCAFi2TOEpnoJcCWwV37cehfbqulY3eAHzJV0LnART65GU1XWhlV9Cv+imPmF0GCSVgFeC2zA0JtZ267ujJek0ztctu3D+x4mDLyY+YXQbBcBDwLzSccIoNKCxrYPK53hqZC0EfBH249KeimwFXBm5TU+wyjFzC+EBpN0q+0tS+cYDUnPA04CdiYN0NcCR9n+Y9FgXUi6CfgP0qz6EmAmqX3Uq0rmCmMjzvmF0GzXS3pB6RCjdDppAHkusC4wK1+r1RN5Z+drgBNtHwM8p3CmMEZi2TOEBmqr5bgScJikhaRNJK1NGTV2cp9su32wmyHp6GJpRrYkn/k7hKGNRRMK5gljKAa/EJqpUf0Hs/skvYGhOqQHUfeh8cOAtwGfsn2npCnAdwpnCmMk7vmFEPoil147GdiJNGu9nnTP766iwTrIbYzOsP2G0lnC8hEzvxBCX+SO7XuXzjEath+XNFnSyrarqpEaxkYMfiGE5UrS+2x/TtJJdDiGYfvIArFG43fAdbm+5+LWRdsnFEsUxkwMfiGE5a1Vy3Nu0RRP3T35YxypqHVYgcQ9vxBC30kaBzzd9sOls4xE0kTbi0f+ztAkcc4vhNAXks6WNCm3W7oN+LWkY0vn6kbSTpJuI89cJW0t6WuFY4UxEoNfCKFfpuaZ3j7Aj4D1gDeWjdTTicAe5OMYthcA04smCmMmBr8QQr9MkDSBNPhdbHsJldYhbbH9h2GXHi8SJIy5GPxCCP1yCmkH5URgTm4aW/M9vz9IehFgSStLei9Dm3dCw8WGlxBCMZJWqrUzuqS1gS8Du5HKxl1KOpRfc1WaMEox+IUQ+kLSUaRC1ouAU4FtgffbvrRosC4kTbb9f6VzhOUjlj1DCP1yeN7wsjswmVQ78zNlI/V0vaRLJb1Z0pqlw4SxFYNfCKFflD+/Cjg9755Uj+8vyvbGwIeBLYD5kn6QC3OHFUAse4YQ+kLS6aQ+flOArYHxwFW2tysabBTy/b8TgINtjy+dJ/z7YvALIfRFruqyDbDQ9oOS1gLWtX1z4WgdSZpEamR7ILARcCHwPdvzigYLYyJqe4YQ+sXAVFIvwuNJRx5WKZqotwXARcDxtm8oHSaMrZj5hRD6QtLXgSeAXW1vLukZwKW2ty8crSNJcrxArrBi5hdC6JcdbE+TdCOA7QckrVw61HCSTrR9NDBTUqcWTI3oSRh6i8EvhNAvS3KHdEM6R0eaCdbm2/nzF4qmCMtVLHuGEPpC0sHA64BpwBnAfsCHbZ9XNFgYSDH4hRCWK0lTbN+Zv94MeDnpfN8VtqutlSlpZ+BjwPqkVTIBtr1hyVxhbMTgF0JYriTNs72dpCtsv7x0ntGS9CvgGGAebd0corbniiHu+YUQlrdxko4DNpH07uFP2j6hQKbReMj2j0uHCMtHDH4hhOXtQFIPv5WA1QtneSpmS/o8cAHwaOui7fnlIoWxEsueIYS+kPTKJs2kJM3OX7ZeJFv3/HYtFCmMoZj5hRD65XpJJwDT8+OrSdVTHiqYqZerOlyL2cIKIro6hBD65TRSL78D8sfDpP5+tfpb28djwCuADUoGCmMnlj1DCH0h6Sbb24x0rVaSngbMtL1H6Szh3xczvxBCvzwiaZfWg3yO7pGCeZ6q1YA447eCiHt+IYR+eRtwpqQ18uMHgEMK5ulJ0i0M3eMbT+o+f3y5RGEsxeAXQljuci+/TW1vnfvkYfvhwrFGsmfb148B99p+rFSYMLbinl8IoS8kzbE9feTvDGH5i8EvhNAXkj5Cusd3LrC4dd32/cVChYEVg18IoS8k3UmHc3JRKDqUEINfCKEvJK0KvAPYhTQIXgN8w3aTdnyGFUQMfiGEvpD0PdLB9rPypYOANW0fUC5VGFQx+IUQ+kLSAttbj3QthH6IQ+4hhH65UdKOrQeSdgCuK5gnDLCY+YUQ+kLS7cCmwO/zpfWA24EnSN0StiqVLQyeGPxCCH0haf1ez9u+q19ZQojBL4QQwsCJe34hhBAGTgx+IYQQBk4MfiGEEAZODH4hhBAGTgx+IYQQBs7/A9t3VipUND1rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b65ea116a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "edu =  [\"illiterate\", \"basic.4y\", \"basic.6y\", \"basic.9y\", \"high.school\",  \"professional.course\", \"university.degree\",\"unknown\"]\n",
    "education_count_yes = bank_data_small[bank_data_small['y']=='yes'].groupby('education').count()['y']\n",
    "education_count_no = bank_data_small[bank_data_small['y']=='no'].groupby('education').count()['y']\n",
    "#按照学历对数据行重新排序\n",
    "education_count_yes = education_count_yes.reindex(index=edu)\n",
    "education_count_no = education_count_no.reindex(index=edu)\n",
    "y = education_count_yes\n",
    "n = education_count_no\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "plt.xticks(rotation=90)\n",
    "ax1.plot(y.values,'b',label = \"Yes\")\n",
    "ax1.set_xticks(np.arange(len(edu)))\n",
    "ax1.set_xticklabels(edu)\n",
    "ax1.set_ylabel('Yes')\n",
    "ax1.set_title(\"Education\")\n",
    "plt.legend()\n",
    "ax2 = ax1.twinx()  # this is the important function\n",
    "ax2.plot(n.values, 'r',label = \"No\")\n",
    "ax2.set_xticks(np.arange(len(edu)))\n",
    "ax2.set_xticklabels(edu)\n",
    "ax2.set_ylabel('No')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6601a4208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "edu = [\"illiterate\", \"basic.4y\", \"basic.6y\", \"basic.9y\", \"high.school\",  \"professional.course\", \"university.degree\",\"unknown\"]\n",
    "\n",
    "education_count_yes = bank_data_small[bank_data_small['y']=='yes'].groupby('education').count()['y']\n",
    "education_count_no = bank_data_small[bank_data_small['y']=='no'].groupby('education').count()['y']\n",
    "#按照学历对数据行重新排序\n",
    "education_count_yes = education_count_yes.reindex(index=edu)\n",
    "education_count_no = education_count_no.reindex(index=edu)\n",
    "\n",
    "index = education_count_yes.index\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "axes=fig.add_subplot(1,1,1)\n",
    "axes.plot((education_count_yes/(education_count_yes+education_count_no)).values,label = 'Yes/(Yes+No)')\n",
    "axes.set_xticks(np.arange(len(edu)))\n",
    "axes.set_xticklabels(edu)\n",
    "axes.set_title(\"Education\")\n",
    "axes.set_ylabel('Yes/(Yes+No)')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b65e608b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration_count_yes = bank_data_small[bank_data_small['y']=='yes'].groupby('duration').count()['y']\n",
    "duration_count_no = bank_data_small[bank_data_small['y']=='no'].groupby('duration').count()['y']\n",
    "#print(duration_count_yes.describe())\n",
    "#print(duration_count_yes.max())\n",
    "#print(duration_count_no.describe())\n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "\n",
    "kwargs = dict(histtype='stepfilled', alpha=0.3, normed=True, bins=30)\n",
    "plt.subplot(121)\n",
    "plt.title(\"Duration\")\n",
    "plt.ylabel('Yes')\n",
    "plt.hist(duration_count_yes,label = \"Yes\", **kwargs)\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.title(\"Duration\")\n",
    "plt.ylabel('No')\n",
    "plt.hist(duration_count_no,label = \"No\", **kwargs)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
